On Some Inner Dependence Relationships in Hierarchical Structure Under Hesitant Fuzzy Environment

Guha, Debatosh; Banerjee, Debasmita

doi:10.1007/978-3-030-19494-9_9

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…In family of averaging operators, Bonferroni mean operator (BM) and its variants play a crucial role as they enable us to handle homogeneous, 16 heterogeneous, 17 partitioned structure interconnections 18 among information during aggregation, and, this propel the application of BM and its variance in MCDM with the focus of capturing various conjunction of the data that arises due to complications in the aggregation systems and human behavior. In particular, such approaches have led to substantial improvements in the potentiality of aggregation operators to model hierarchical data aggregation 19 . Further, if big data analysis and mining are taken as an example, then data can be analyzed using a different methodology, one of which is the partition algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, such approaches have led to substantial improvements in the potentiality of aggregation operators to model hierarchical data aggregation. 19 Further, if big data analysis and mining are taken as an example, then data can be analyzed using a different methodology, one of which is the partition algorithm. As there are wide varieties of valuable data, thus to analyze and interpret them, partition algorithm is used to divide the databases into several partition sets and based on this partition structure, representative data can be obtained.…”

Section: Introductionmentioning

confidence: 99%

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Generalization and extension of partitioned Bonferroni mean operator to model optional prerequisites

et al. 2020

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The partitioned Bonferroni mean (PBM) operator, which was oriented as an elementary attempt to outstretch the Bonferroni mean (BM) operator, has enlarged the class of BM‐type aggregation operators for information accumulation by modeling interrelationship among pairwise disjoint partition sets with the presupposition that the criteria of intra‐partition are homogeneously related to each other, while no relationship exists among criteria of inter‐partition. Although PBM has encountered a lot of attraction from the researchers due to its versatility in information aggregation technique, the principal disadvantage of the existing PBM definitions evolution is that they do not provide any specification regarding the relationship among criteria of partition structure during design, development, and applications of PBM over unalike situations of information fusion. This consideration propels us to focus on the systematic investigation of different variations of PBM operators based on various mandatory requisites to be imposed on information retrieved from the partition sets. In this regard, we propose the construction of novel generalized partitioned Bonferroni mean (GPBM) operator by befitting its suitable components to provide a descriptive configuration, which is quite interpretable, understandable and thus facilitates the ability to model specific mandatory prerequisites in a single operator. To enrich the capacity for modeling real‐life decision situations, the PBM operator is customized to propose optional partitioned Bonferroni mean (OPBM) operator that captures partition‐wise interrelationship among attributes while taking into consideration optional conditions jumbled in each partition set. Furthermore, we demonstrate the construction methodology of generalized OPBM operator that amalgamate the concept of GPBM and OPBM operator to enhance and model‐specific requirements along with optional requirements as per the desires of decision makers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generalization and extension of partitioned Bonferroni mean operator to model optional prerequisites

et al. 2020

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Multi-dimensional data aggregation utilizing extended partitioned Bonferroni mean Operator

Banerjee

Guha

Mesiar

2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Multiple Hierarchically Structured Criteria in ARAS Method Under Fuzzy Environment

Ghram

Frikha

2022

International Journal of Fuzzy System Applications

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The aim of multiple criteria decision aiding (MCDA) is to assist decision makers (DMs) to make rational decisions with respect to their preferences. In fact, the ranking approaches are the most used ones nowadays in the MCDA field because they are easy to understand by DMs and they are based on realistic assumptions. The hierarchical additive ratio assessment (ARAS-H) method is a ranking method. It represents an extension of the ARAS method in case of hierarchically structured criteria. However, most often, the DM is unable to provide precise performance values. Henceforth, in order to facilitate the task for him, he is asked to provide linguistic variables. Thus, the authors adopted the fuzzy logic. As a matter of fact, the fuzzy set theory takes into account the subjectivity of experts' ‘judgments.' In the light of the above, the fuzzy ARAS-H (F-ARAS-H) algorithm was developed as an extension of the ARAS-H method in a context of a fuzzy environment. To discuss the feasibility of the proposed algorithm, a case study on the selection of a green supplier was presented.

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On Some Inner Dependence Relationships in Hierarchical Structure Under Hesitant Fuzzy Environment

Cited by 3 publications

References 14 publications

Generalization and extension of partitioned Bonferroni mean operator to model optional prerequisites

Generalization and extension of partitioned Bonferroni mean operator to model optional prerequisites

Multi-dimensional data aggregation utilizing extended partitioned Bonferroni mean Operator

Multiple Hierarchically Structured Criteria in ARAS Method Under Fuzzy Environment

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